Models with structured additive predictor provide a very broad and rich framework for complex regression modeling. They can deal simultaneously with nonlinear covariate effects and time trends, unit- or cluster specific heterogeneity, spatial heterogeneity and complex interactions between...

What are the factors that favor or slow down the use of the Internet in Africa? In order to answer this question, research has multiplied over the past years. This study proposes a two level hierarchical modeling of the explanatory factors of the Internet adoption in Africa. The data used come...

Meta-analysis of diagnostic test accuracy presents many challenges. Even in the simplest case, when the data are summarized by a 2 × 2 table from each study, a statistically rigorous analysis requires hierarchical (multilevel) models that respect the binomial data structure, such as...

In this paper we investigate the effect of class attendance on academic performance, and evaluate the existence and importance of minimum attendance requirement thresholds. We found that attendance has a relevant and statistically significant impact on performance, together with the existence of...

Many kinds of data in the social sciences have a hierarchical, multilevel or clustered structure. For example, municipalities are grouped into regions; regions are formed within countries; and quite often, countries belong to supra-national organizations. Once groupings are established, they...

A Bayesian hierarchical model for simultaneously estimating and partitioning probability density functions is presented. Individual density functions are flexibly modeled using Bernstein densities, which are mixtures of beta densities whose parameters depend only on the number of mixture...

In random effect models, error variance (stage 1 variance) and scalar random effect variance components (stage 2 variances) are a priori modeled independently. Considering the intrinsic link between the stages 1 and 2 variance components and their interactive effect on the parameter draws in...

In this thesis, we address issues of model estimation for longitudinal categorical data and of model selection for these data with missing covariates. Longitudinal survey data capture the responses of each subject repeatedly through time, allowing for the separation of variation in the measured...